{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 时间序列分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline   \n",
    "\n",
    "#正常显示画图时出现的中文和负号\n",
    "from pylab import mpl\n",
    "mpl.rcParams['font.sans-serif']=['SimHei']\n",
    "mpl.rcParams['axes.unicode_minus']=False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "时间类型及其在python中对应的类型\n",
    "- 时间戳：timestamp\n",
    "- 时间间隔：timedelta\n",
    "- 时期：period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2019', 'A-DEC')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p=pd.Period(2019,freq='A-DEC')\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2014', 'A-DEC')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p-5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "period_range函数创建时期范围\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "date=pd.period_range('2018-01-01','2019-01-10',freq='Q')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018Q1    0.494494\n",
       "2018Q2   -0.001953\n",
       "2018Q3   -1.231664\n",
       "2018Q4    0.875689\n",
       "2019Q1   -0.010776\n",
       "Freq: Q-DEC, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.Series(np.random.randn(len(date)),index=date)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2018Q1', '2018Q2', '2018Q3'], dtype='period[Q-DEC]', freq='Q-DEC')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PeriodIndex类的构造函数允许直接使用一组字符串表示一段时期\n",
    "values = ['2018Q1','2018Q2','2018Q3']\n",
    "index = pd.PeriodIndex(values,freq='Q-DEC')\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# datetime处理日期"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "python常用的处理时间的库有：datetime,time,calendar。datetime库包括了date（储存日期：(年、月、日),time(储存时间：(小时、分、秒和微秒)，datetime同时包含了data和time，timedelta代表两个datetime之间的差（天、秒、微秒）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当前时间：2019-01-10 21:24:23.101667\n",
      "2019年1月10日\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "now=datetime.now()\n",
    "print(f'当前时间：{now}')\n",
    "print(f'{now.year}年{now.month}月{now.day}日')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2019-01-10'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "timedelta计算相差天数和秒数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(days=8, seconds=41400)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta=datetime(2019,1,10)-datetime(2019,1,1,12,30)\n",
    "delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2018, 2, 20, 0, 0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import timedelta\n",
    "start=datetime(2018,1,1)\n",
    "#计算50天后是哪一天\n",
    "start+timedelta(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(days=9)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#字符串和时间的转化\n",
    "#比如想要知道列表里两个时间字符串之间相差多少天\n",
    "datestr=['12/20/2018','12/11/2018']\n",
    "new_date=[datetime.strptime(d,'%m/%d/%Y') for d in datestr]\n",
    "new_date[0]-new_date[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2018-12-20', '2018-12-11']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将datetime格式转换为常见的年（Y）月（m）日（d）格式表示\n",
    "[date.strftime('%Y-%m-%d') for date in new_date]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "datetime.strptime只能根据设定的时间格式来处理指定的字符串，如果列表里(list)包含不止一种格式的字符串，如datestr=\\['12/20/2018','12/11/2018','2018-10-18'\\],使用datetime.strptime就很难处理了。遇到这种情况可以引入第三方时间处理包dateutil，可以处理任意格式字符串。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datetime格式：\n",
      "[datetime.datetime(2018, 12, 20, 0, 0), datetime.datetime(2018, 2, 10, 0, 0), datetime.datetime(2019, 1, 10, 0, 0)]\n",
      "\"月/日/年\"格式：\n",
      " ['12/20/2018', '02/10/2018', '01/10/2019']\n",
      "\"年月日\"格式：\n",
      "['20181220', '20180210', '20190110']\n",
      "\"年-月-日格式\"：\n",
      "['2018-12-20', '2018-02-10', '2019-01-10']\n",
      "\"年（后两位）-月-日\"格式：\n",
      "['18-12-20', '18-02-10', '19-01-10']\n"
     ]
    }
   ],
   "source": [
    "from dateutil.parser import parse\n",
    "datestr=['12/20/2018','20180210','2019-01-10']\n",
    "#转换成datetime格式\n",
    "new_d=[parse(d) for d in datestr]\n",
    "#统一为12/20/2018格式\n",
    "d1=[d.strftime('%m/%d/%Y') for d in new_d]\n",
    "d2=[d.strftime('%Y%m%d') for d in new_d]\n",
    "d3=[d.strftime('%Y-%m-%d') for d in new_d]\n",
    "d4=[d.strftime('%y-%m-%d') for d in new_d]\n",
    "print(f'datetime格式：\\n{new_d}')\n",
    "print(f'\"月/日/年\"格式：\\n {d1}')\n",
    "print(f'\"年月日\"格式：\\n{d2}')\n",
    "print(f'\"年-月-日格式\"：\\n{d3}')\n",
    "print(f'\"年（后两位）-月-日\"格式：\\n{d4}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用NumPy库处理日期\n",
    "numpy库的时间格式是datetime64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2019-01-10')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将字符串转换成numpy格式时间\n",
    "#注意个位前补0，如1月写成01\n",
    "nd=np.datetime64('2019-01-10')\n",
    "nd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2019-01-10'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#转化为字符串\n",
    "np.datetime_as_string(nd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('1901')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.datetime64('1901')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2019, 1, 10)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#转化为datetime格式\n",
    "nd.astype(datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2019-01-05', '2019-01-06', '2019-01-07', '2019-01-08',\n",
       "       '2019-01-09'], dtype='datetime64[D]')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#生成时间序列\n",
    "#默认以日为间隔，算头不算尾\n",
    "np.arange('2019-01-05','2019-01-10',dtype='datetime64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2018-01', '2018-02', '2018-03', '2018-04', '2018-05', '2018-06',\n",
       "       '2018-07', '2018-08', '2018-09', '2018-10', '2018-11', '2018-12'],\n",
       "      dtype='datetime64[M]')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#以月为间隔，生成2018年12个月\n",
    "np.arange('2018-01-01','2019-01-01',dtype='datetime64[M]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2015', '2016', '2017', '2018'], dtype='datetime64[Y]')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#以年为间隔\n",
    "np.arange('2015-01-01','2019-01-20',dtype='datetime64[Y]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2018-11-29', '2018-12-06', '2018-12-13'], dtype='datetime64[W]')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#以周为间隔\n",
    "np.arange('2018-12-01','2018-12-20',dtype='datetime64[W]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#设定随机种子（括号里的数字只是起标记作用）\n",
    "np.random.seed(1)\n",
    "#h:小时，m:分，s：秒，ms微秒\n",
    "#生成分时\n",
    "x=np.arange('2019-01-10T00:00:00','2019-01-10T23:00:00',dtype='datetime64[m]')\n",
    "#生成标准正态分布时间序列\n",
    "y=np.random.standard_normal(len(x))\n",
    "#设置图片大小\n",
    "fig=plt.figure(figsize=(12,6))\n",
    "#将x的np.datetime转换为datetime.datetime\n",
    "plt.plot(x.astype(datetime),y)\n",
    "fig.autofmt_xdate()\n",
    "plt.title('模拟23小时内每分钟正态分布的随机数分布')\n",
    "# 将右边 上边的两条边颜色设置为空 其实就相当于抹掉这两条边\n",
    "ax = plt.gca()  \n",
    "ax.spines['right'].set_color('none') \n",
    "ax.spines['top'].set_color('none')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas库处理日期\n",
    "Pandas包可以说是为处理时间序列而生的。日期时间在pandas储存的格式为Timestamp。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-01-10 00:00:00')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=pd.Timestamp('2019-01-10')\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2019-01-10T00:00:00.000000000')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将Timestamp转换为numpy.datetime64\n",
    "d = t.to_datetime64()\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-12-01', '2018-12-02', '2018-12-03'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#转化为Timestamp\n",
    "dstr=['20181201','20181202','20181203']\n",
    "#DatetimeIndex\n",
    "pd.to_datetime(dstr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',\n",
       "               '2018-05-31', '2018-06-30', '2018-07-31', '2018-08-31',\n",
       "               '2018-09-30', '2018-10-31', '2018-11-30', '2018-12-31'],\n",
       "              dtype='datetime64[ns]', freq='M')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用date_range生成日期序列\n",
    "dt = pd.date_range('2018/01/01', freq='M', periods=12)\n",
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-03-31 00:00:00', freq='M')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([datetime.datetime(2018, 1, 31, 0, 0),\n",
       "       datetime.datetime(2018, 2, 28, 0, 0),\n",
       "       datetime.datetime(2018, 3, 31, 0, 0),\n",
       "       datetime.datetime(2018, 4, 30, 0, 0),\n",
       "       datetime.datetime(2018, 5, 31, 0, 0),\n",
       "       datetime.datetime(2018, 6, 30, 0, 0),\n",
       "       datetime.datetime(2018, 7, 31, 0, 0),\n",
       "       datetime.datetime(2018, 8, 31, 0, 0),\n",
       "       datetime.datetime(2018, 9, 30, 0, 0),\n",
       "       datetime.datetime(2018, 10, 31, 0, 0),\n",
       "       datetime.datetime(2018, 11, 30, 0, 0),\n",
       "       datetime.datetime(2018, 12, 31, 0, 0)], dtype=object)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#转为化datetime对象\n",
    "dty=dt.to_pydatetime()\n",
    "dty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',\n",
       "               '2018-05-31', '2018-06-30', '2018-07-31', '2018-08-31',\n",
       "               '2018-09-30', '2018-10-31', '2018-11-30', '2018-12-31'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#转化回pdans的视角类型\n",
    "pd.DatetimeIndex(dty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画以时间为x轴的图,pandas的DataFrame自动将index列作为x轴\n",
    "np.random.seed(2)\n",
    "#生成日期序列\n",
    "x=pd.date_range('2018/01/01','2019/12/31', freq='d')\n",
    "#标准正态分布时间序列\n",
    "y=np.random.standard_normal(len(x))\n",
    "#将二者转换为pandas的数据格式\n",
    "df=pd.DataFrame(y,columns=['标准正态分布'],index=x)\n",
    "df.plot(figsize=(12,6))\n",
    "plt.title('模拟标准正态分布随机数')\n",
    "\n",
    "ax = plt.gca()  \n",
    "ax.spines['right'].set_color('none') \n",
    "ax.spines['top'].set_color('none')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 时间序列的样本重采样\n",
    "时间序列样本转换主要分两种：即高频数据向低频数据转换；低频数据向高频数据转换。  \n",
    "主要函数:df.resample()，df代表pandas的DataFrame格式数据，resample方法的参数参数中，freq表示重采样频率,例如‘M’、‘5min’,Second(15)，用于产生聚合值的函数名或数组函数,例如‘mean’、‘ohlc’、np.max等,默认是‘mean’,其他常用的有:‘first’、‘last’、‘median’、‘max’、‘min’axis=0默认是纵轴,横轴设置axis=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 高频数据向低频数据转化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>时间</th>\n",
       "      <th>成交</th>\n",
       "      <th>现手</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09:25:00</td>\n",
       "      <td>2543.85</td>\n",
       "      <td>169975700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2544.64</td>\n",
       "      <td>118762900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2544.34</td>\n",
       "      <td>67005000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2543.88</td>\n",
       "      <td>25638400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2542.83</td>\n",
       "      <td>27602100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2541.97</td>\n",
       "      <td>16822900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2542.48</td>\n",
       "      <td>18605000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2541.34</td>\n",
       "      <td>19279300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2541.32</td>\n",
       "      <td>13209000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2542.49</td>\n",
       "      <td>18115500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         时间       成交         现手\n",
       "0  09:25:00  2543.85  169975700\n",
       "1  09:30:00  2544.64  118762900\n",
       "2  09:30:00  2544.34   67005000\n",
       "3  09:30:00  2543.88   25638400\n",
       "4  09:30:00  2542.83   27602100\n",
       "5  09:30:00  2541.97   16822900\n",
       "6  09:30:00  2542.48   18605000\n",
       "7  09:30:00  2541.34   19279300\n",
       "8  09:30:00  2541.32   13209000\n",
       "9  09:30:00  2542.49   18115500"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#导入2019年1月10日上证指数的分时数据\n",
    "#数据来源：同花顺\n",
    "df=pd.read_excel('Table.xlsx')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>时间</th>\n",
       "      <th>成交</th>\n",
       "      <th>现手</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>时间</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>09:25:00</th>\n",
       "      <td>09:25:00</td>\n",
       "      <td>2543.85</td>\n",
       "      <td>169975700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09:30:00</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2544.64</td>\n",
       "      <td>118762900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09:30:00</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2544.34</td>\n",
       "      <td>67005000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09:30:00</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2543.88</td>\n",
       "      <td>25638400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09:30:00</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2542.83</td>\n",
       "      <td>27602100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                时间       成交         现手\n",
       "时间                                    \n",
       "09:25:00  09:25:00  2543.85  169975700\n",
       "09:30:00  09:30:00  2544.64  118762900\n",
       "09:30:00  09:30:00  2544.34   67005000\n",
       "09:30:00  09:30:00  2543.88   25638400\n",
       "09:30:00  09:30:00  2542.83   27602100"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.set_index(df['时间'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['成交'].plot(figsize=(16,6),label='成交价格')\n",
    "plt.title('上证综指2019年1月10日分时图',fontsize=15)\n",
    "ax = plt.gca()  \n",
    "ax.spines['right'].set_color('none') \n",
    "ax.spines['top'].set_color('none')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>时间</th>\n",
       "      <th>成交</th>\n",
       "      <th>现手</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>时间</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1900-01-01 09:25:00</th>\n",
       "      <td>09:25:00</td>\n",
       "      <td>2543.85</td>\n",
       "      <td>169975700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-01-01 09:30:00</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2544.64</td>\n",
       "      <td>118762900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-01-01 09:30:00</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2544.34</td>\n",
       "      <td>67005000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-01-01 09:30:00</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2543.88</td>\n",
       "      <td>25638400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-01-01 09:30:00</th>\n",
       "      <td>09:30:00</td>\n",
       "      <td>2542.83</td>\n",
       "      <td>27602100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           时间       成交         现手\n",
       "时间                                               \n",
       "1900-01-01 09:25:00  09:25:00  2543.85  169975700\n",
       "1900-01-01 09:30:00  09:30:00  2544.64  118762900\n",
       "1900-01-01 09:30:00  09:30:00  2544.34   67005000\n",
       "1900-01-01 09:30:00  09:30:00  2543.88   25638400\n",
       "1900-01-01 09:30:00  09:30:00  2542.83   27602100"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index=pd.to_datetime(df.index,format='%H:%M:%S')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "时间\n",
       "1900-01-01 09:25:00    2543.85\n",
       "1900-01-01 09:30:00    2544.64\n",
       "1900-01-01 09:30:00    2544.34\n",
       "1900-01-01 09:30:00    2543.88\n",
       "1900-01-01 09:30:00    2542.83\n",
       "Name: 成交, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts=df['成交']\n",
    "ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "时间\n",
       "1900-01-01 09:00:00    2539.635000\n",
       "1900-01-01 10:00:00    2541.493264\n",
       "1900-01-01 11:00:00    2544.911878\n",
       "1900-01-01 12:00:00            NaN\n",
       "1900-01-01 13:00:00    2546.391266\n",
       "1900-01-01 14:00:00    2539.339170\n",
       "1900-01-01 15:00:00    2535.080000\n",
       "Freq: H, Name: 成交, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将其转换为每5分钟样本,默认closed='left',label='left'\n",
    "ts.resample('H').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-12-31 23:55:00     0\n",
       "2018-01-01 00:00:00    15\n",
       "2018-01-01 00:05:00    40\n",
       "2018-01-01 00:10:00    11\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min',closed='right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-01 00:00:00     0\n",
       "2018-01-01 00:05:00    15\n",
       "2018-01-01 00:10:00    40\n",
       "2018-01-01 00:15:00    11\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min',closed='right',label='right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-01 00:05:00    10\n",
       "2018-01-01 00:10:00    35\n",
       "2018-01-01 00:15:00    21\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min',closed='left',label='right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-12-31 23:59:59     0\n",
       "2018-01-01 00:04:59    15\n",
       "2018-01-01 00:09:59    40\n",
       "2018-01-01 00:14:59    11\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min',closed='right',label='right',loffset='-1s').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 根据groupby进行resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    15.0\n",
       "2    44.5\n",
       "3    74.0\n",
       "4    94.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('1/1/2018', periods=100, freq='D')\n",
    "ts=pd.Series(np.arange(100),index=rng)\n",
    "#按月进行汇总求平均值\n",
    "ts.groupby(lambda x:x.month).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    49.0\n",
       "1    50.0\n",
       "2    47.5\n",
       "3    48.5\n",
       "4    49.5\n",
       "5    50.5\n",
       "6    51.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按周进行汇总求平均值\n",
    "ts.groupby(lambda x:x.weekday).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 低频数据向高频数据转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(np.random.randn(2,4),\n",
    "            index=pd.date_range('1/1/2018',periods=2,freq='W-WED'),\n",
    "            columns=['GZ','BJ','SH','SZ'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GZ</th>\n",
       "      <th>BJ</th>\n",
       "      <th>SH</th>\n",
       "      <th>SZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>0.041539</td>\n",
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       "      <td>0.539058</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
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       "      <td>-0.747871</td>\n",
       "      <td>0.009025</td>\n",
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      ],
      "text/plain": [
       "                  GZ        BJ        SH        SZ\n",
       "2018-01-03  0.041539 -1.117925  0.539058 -0.596160\n",
       "2018-01-10 -0.019130  1.175001 -0.747871  0.009025"
      ]
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     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>GZ</th>\n",
       "      <th>BJ</th>\n",
       "      <th>SH</th>\n",
       "      <th>SZ</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
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       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0.041539</td>\n",
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       "      <td>0.539058</td>\n",
       "      <td>-0.596160</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.041539</td>\n",
       "      <td>-1.117925</td>\n",
       "      <td>0.539058</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>0.041539</td>\n",
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       "      <td>0.539058</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-07</th>\n",
       "      <td>0.041539</td>\n",
       "      <td>-1.117925</td>\n",
       "      <td>0.539058</td>\n",
       "      <td>-0.596160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>0.041539</td>\n",
       "      <td>-1.117925</td>\n",
       "      <td>0.539058</td>\n",
       "      <td>-0.596160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>0.041539</td>\n",
       "      <td>-1.117925</td>\n",
       "      <td>0.539058</td>\n",
       "      <td>-0.596160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>-0.019130</td>\n",
       "      <td>1.175001</td>\n",
       "      <td>-0.747871</td>\n",
       "      <td>0.009025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  GZ        BJ        SH        SZ\n",
       "2018-01-03  0.041539 -1.117925  0.539058 -0.596160\n",
       "2018-01-04  0.041539 -1.117925  0.539058 -0.596160\n",
       "2018-01-05  0.041539 -1.117925  0.539058 -0.596160\n",
       "2018-01-06  0.041539 -1.117925  0.539058 -0.596160\n",
       "2018-01-07  0.041539 -1.117925  0.539058 -0.596160\n",
       "2018-01-08  0.041539 -1.117925  0.539058 -0.596160\n",
       "2018-01-09  0.041539 -1.117925  0.539058 -0.596160\n",
       "2018-01-10 -0.019130  1.175001 -0.747871  0.009025"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将上述样本转换为日序列,缺失值使用前值补上\n",
    "df_daily=df.resample('D').ffill()\n",
    "df_daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>BJ</th>\n",
       "      <th>SH</th>\n",
       "      <th>SZ</th>\n",
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       "      <th>2018-01-06</th>\n",
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       "      <th>2018-01-07</th>\n",
       "      <td>-0.019130</td>\n",
       "      <td>1.175001</td>\n",
       "      <td>-0.747871</td>\n",
       "      <td>0.009025</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>-0.019130</td>\n",
       "      <td>1.175001</td>\n",
       "      <td>-0.747871</td>\n",
       "      <td>0.009025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>-0.019130</td>\n",
       "      <td>1.175001</td>\n",
       "      <td>-0.747871</td>\n",
       "      <td>0.009025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>-0.019130</td>\n",
       "      <td>1.175001</td>\n",
       "      <td>-0.747871</td>\n",
       "      <td>0.009025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  GZ        BJ        SH        SZ\n",
       "2018-01-03  0.041539 -1.117925  0.539058 -0.596160\n",
       "2018-01-04 -0.019130  1.175001 -0.747871  0.009025\n",
       "2018-01-05 -0.019130  1.175001 -0.747871  0.009025\n",
       "2018-01-06 -0.019130  1.175001 -0.747871  0.009025\n",
       "2018-01-07 -0.019130  1.175001 -0.747871  0.009025\n",
       "2018-01-08 -0.019130  1.175001 -0.747871  0.009025\n",
       "2018-01-09 -0.019130  1.175001 -0.747871  0.009025\n",
       "2018-01-10 -0.019130  1.175001 -0.747871  0.009025"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将上述样本转换为日序列,缺失值使用后值补上\n",
    "df_daily=df.resample('D').bfill()\n",
    "df_daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>GZ</th>\n",
       "      <th>BJ</th>\n",
       "      <th>SH</th>\n",
       "      <th>SZ</th>\n",
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       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
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       "      <td>0.539058</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>-0.019130</td>\n",
       "      <td>1.175001</td>\n",
       "      <td>-0.747871</td>\n",
       "      <td>0.009025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  GZ        BJ        SH        SZ\n",
       "2018-01-03  0.041539 -1.117925  0.539058 -0.596160\n",
       "2018-01-04       NaN       NaN       NaN       NaN\n",
       "2018-01-05       NaN       NaN       NaN       NaN\n",
       "2018-01-06       NaN       NaN       NaN       NaN\n",
       "2018-01-07       NaN       NaN       NaN       NaN\n",
       "2018-01-08       NaN       NaN       NaN       NaN\n",
       "2018-01-09       NaN       NaN       NaN       NaN\n",
       "2018-01-10 -0.019130  1.175001 -0.747871  0.009025"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_daily=df.resample('D').mean()\n",
    "df_daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "#根据period来重采样\n",
    "df=pd.DataFrame(np.random.randn(24,4),\n",
    "            index=pd.period_range('1-2017','12-2018',freq='M'),\n",
    "            columns=['GZ','BJ','SH','SZ'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GZ</th>\n",
       "      <th>BJ</th>\n",
       "      <th>SH</th>\n",
       "      <th>SZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01</th>\n",
       "      <td>-0.878108</td>\n",
       "      <td>-0.156434</td>\n",
       "      <td>0.256570</td>\n",
       "      <td>-0.988779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02</th>\n",
       "      <td>-0.338822</td>\n",
       "      <td>-0.236184</td>\n",
       "      <td>-0.637655</td>\n",
       "      <td>-1.187612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03</th>\n",
       "      <td>-1.421217</td>\n",
       "      <td>-0.153495</td>\n",
       "      <td>-0.269057</td>\n",
       "      <td>2.231367</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "               GZ        BJ        SH        SZ\n",
       "2017-01 -0.878108 -0.156434  0.256570 -0.988779\n",
       "2017-02 -0.338822 -0.236184 -0.637655 -1.187612\n",
       "2017-03 -1.421217 -0.153495 -0.269057  2.231367"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GZ</th>\n",
       "      <th>BJ</th>\n",
       "      <th>SH</th>\n",
       "      <th>SZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>-0.643001</td>\n",
       "      <td>-0.033892</td>\n",
       "      <td>-0.050309</td>\n",
       "      <td>0.138446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>-0.312618</td>\n",
       "      <td>0.215501</td>\n",
       "      <td>0.087790</td>\n",
       "      <td>0.159978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            GZ        BJ        SH        SZ\n",
       "2017 -0.643001 -0.033892 -0.050309  0.138446\n",
       "2018 -0.312618  0.215501  0.087790  0.159978"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#A-DEC: annually, year ending in December\n",
    "df_annual=df.resample('A-DEC').mean()\n",
    "df_annual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GZ</th>\n",
       "      <th>BJ</th>\n",
       "      <th>SH</th>\n",
       "      <th>SZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017Q1</th>\n",
       "      <td>-0.643001</td>\n",
       "      <td>-0.033892</td>\n",
       "      <td>-0.050309</td>\n",
       "      <td>0.138446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017Q2</th>\n",
       "      <td>-0.643001</td>\n",
       "      <td>-0.033892</td>\n",
       "      <td>-0.050309</td>\n",
       "      <td>0.138446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017Q3</th>\n",
       "      <td>-0.643001</td>\n",
       "      <td>-0.033892</td>\n",
       "      <td>-0.050309</td>\n",
       "      <td>0.138446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017Q4</th>\n",
       "      <td>-0.643001</td>\n",
       "      <td>-0.033892</td>\n",
       "      <td>-0.050309</td>\n",
       "      <td>0.138446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018Q1</th>\n",
       "      <td>-0.312618</td>\n",
       "      <td>0.215501</td>\n",
       "      <td>0.087790</td>\n",
       "      <td>0.159978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018Q2</th>\n",
       "      <td>-0.312618</td>\n",
       "      <td>0.215501</td>\n",
       "      <td>0.087790</td>\n",
       "      <td>0.159978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018Q3</th>\n",
       "      <td>-0.312618</td>\n",
       "      <td>0.215501</td>\n",
       "      <td>0.087790</td>\n",
       "      <td>0.159978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018Q4</th>\n",
       "      <td>-0.312618</td>\n",
       "      <td>0.215501</td>\n",
       "      <td>0.087790</td>\n",
       "      <td>0.159978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              GZ        BJ        SH        SZ\n",
       "2017Q1 -0.643001 -0.033892 -0.050309  0.138446\n",
       "2017Q2 -0.643001 -0.033892 -0.050309  0.138446\n",
       "2017Q3 -0.643001 -0.033892 -0.050309  0.138446\n",
       "2017Q4 -0.643001 -0.033892 -0.050309  0.138446\n",
       "2018Q1 -0.312618  0.215501  0.087790  0.159978\n",
       "2018Q2 -0.312618  0.215501  0.087790  0.159978\n",
       "2018Q3 -0.312618  0.215501  0.087790  0.159978\n",
       "2018Q4 -0.312618  0.215501  0.087790  0.159978"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q-DEC: Quarterly, decenber\n",
    "df_annual.resample('Q-DEC').ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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